UnitNorm: Rethinking Normalization for Transformers in Time Series

Abstract

Normalization techniques are crucial for enhancing Transformer models’ performance and stability in time series analysis tasks, yet traditional methods like batch and layer normalization often lead to issues such as token shift, attention shift, and sparse attention. We propose UnitNorm, a novel approach that scales input vectors by their norms and modulates attention patterns, effectively circumventing these challenges. Grounded in existing normalization frameworks, UnitNorm’s effective- ness is demonstrated across diverse time series analysis tasks, including forecasting, classification, and anomaly detection, via a rigorous evaluation on 6 state-of-the-art models and 10 datasets. Notably, UnitNorm shows superior performance, especially in scenarios requiring robust attention mechanisms and contextual comprehension, evidenced by significant improvements by up to a 1.46 decrease in MSE for forecast- ing, and a 4.89% increase in accuracy for classification. This work not only calls for a reevaluation of normalization strategies in time series Transformers but also sets a new direction for enhancing model performance and stability.